Element-wise maths and broadcasting rules.
✍️ 3 exercises + code challenge in this lessonArithmetic works element by element: a + b, a * 2, np.sqrt(a).
Broadcasting lets NumPy combine arrays of different shapes, e.g. adding a row to every row of a matrix.
Universal functions (ufuncs): np.add, np.power, np.round, np.exp, np.log.
Mastering NumPy Math & Broadcasting helps you write clean and reliable NumPy code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.
import numpy as np
prices = np.array([100, 250, 400])
print(prices * 1.18)
print(np.sqrt(np.array([16, 25, 36])))
m = np.array([[1, 2, 3], [4, 5, 6]])
print(m + np.array([10, 20, 30]))[118. 295. 472.] [4. 5. 6.] [[11 22 33] [14 25 36]]
Check what you learned in “NumPy Math & Broadcasting”.
Square root of every element.
np.(arr)
What is broadcasting?
Multiply every price by 2 and print the result as a list.
Free sign-in required · Google login available
Stuck on something?
Ask the community in the CodesInTech forum.
Need a developer?
Learning on your own is great — and when you need professional help, our team builds custom software, websites and mobile apps for clients worldwide.
Explore Our Services